Depth information in deep learning systems can be represented with, e.g., voxel grids [1]}, [2]}, truncated signed distance functions (TSDF) [3]}, or point clouds [4]}.
Voxel grids are simple to generate and use.
Because of their regular grid structure, voxel grids can be directly used as inputs to 3D CNNs.
However, voxel grids are inefficient since they also have to explicitly represent empty space.
They also suffer from discretization artifacts.
TSDF tries to alleviate these problems by storing the shortest distance to the surface represented in each voxel.
This allows a more faithful representation of the 3D information.
In comparison to other depth data representations, a point cloud has a simple representation without redundancy, yet contains rich geometric information.
Recently, PointNet [4]} has allowed to use raw point clouds directly as an input of a CNN.
